Fall risk is high for older adults with dementia. Gait impairment contributes to increased fall risk, and gait changes are common in people with dementia, although the reliable assessment of gait is challenging in this population. This study aimed to develop an automated approach to performing gait assessments based on gait data that is collected frequently and unobtrusively, and analysed using computer vision methods. Recent developments in computer vision have led to the availability of open source human pose estimation algorithms, which automatically estimate the joint locations of a person in an image. In this study, a pre-existing pose estimation model was applied to 1066 walking videos collected of 31 older adults with dementia as they walked naturally in a corridor on a specialized dementia unit over a two week period. Using the tracked pose information, gait features were extracted from video recordings of gait bouts and their association with clinical mobility assessment scores and future falls data was examined. A significant association was found between extracted gait features and a clinical mobility assessment and the number of future falls, providing concurrent and predictive validation of this approach.

中文翻译：

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使用计算机视觉测量步态变量来评估患有痴呆症的老年人的活动能力和跌倒风险


患有痴呆症的老年人跌倒的风险很高。步态障碍会增加跌倒风险，并且步态变化在痴呆症患者中很常见，尽管对该人群的步态进行可靠评估具有挑战性。本研究旨在开发一种基于频繁且不引人注目地收集的步态数据来执行步态评估的自动化方法，并使用计算机视觉方法进行分析。计算机视觉的最新发展导致了开源人体姿势估计算法的出现，该算法可以自动估计图像中人的关节位置。在这项研究中，我们将预先存在的姿势估计模型应用于 1066 个步行视频，这些视频收集了 31 名患有痴呆症的老年人在两周内在专门的痴呆症治疗室的走廊里自然行走的视频。使用跟踪的姿势信息，从步态发作的视频记录中提取步态特征，并检查它们与临床活动能力评估分数和未来跌倒数据的关联。我们发现提取的步态特征和临床活动能力评估以及未来跌倒次数之间存在显着关联，从而为该方法提供了并发和预测验证。